MétaCan
Menu
Back to cohort
Record W4386379687 · doi:10.1024/1861-6186/a000755

Simply the best?

2023· article· de· W4386379687 on OpenAlexaff
Michaela Key, Franziska Tschirky Feratovic

Bibliographic record

VenuePADUA · 2023
Typearticle
Languagede
FieldHealth Professions
TopicHealth and Medical Studies
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Zusammenfassung: Welche Kandidat_innen passen am besten zu uns? Wer hat das Potenzial, die Ausbildung erfolgreich abzuschließen? Fragen wie diese sind entscheidend für Lehrbetriebe – besonders bei hohen Bewerberzahlen. Um neue Wege bei der Lernendenauswahl zu gehen, haben wir im Universitätsspital Zürich (USZ) das Verfahren genau auf die Versorgungserfordernisse des USZ abgestimmt.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.906
Threshold uncertainty score0.315

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.028
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0070.008
Scholarly communication0.0130.014
Open science0.0010.006
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0940.034

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.187
GPT teacher head0.494
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venuePADUASame topicHealth and Medical StudiesFrench-language works237,207